Since the discovery of oceanic manganese nodules during the expedition of the British ocean-going ship Challenger from 1872 to 1876, research and development for seabed manganese nodules have never ceased owing to the...Since the discovery of oceanic manganese nodules during the expedition of the British ocean-going ship Challenger from 1872 to 1876, research and development for seabed manganese nodules have never ceased owing to the huge economic inducements. Manganese nodules are the black or dark brown, spherical or massive Mn-bearing ores, deposits of which are found on the sea bottom. The nodules are a mixture of silicate and insoluble potassium permanganates （also with sub-Ti, Fe and Na permanganates） that contain more than 30 kinds of metallic elements, among which those of greatest economic interest are Mn （27-30%）, Ni（1.25-1.5%）, Cu（1- 1.4%）, Co（0.2- 0.25 %）, Fe, Si, and AI, with minor amounts of Ca, Na, K, Ti, B, H and O.展开更多
The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient e...The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.展开更多
Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to r...Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.展开更多
Renewable energy includes all forms of energy produced from renewable sources in a sustainable manner, including bioenergy, geothermal energy, hydropower, ocean energy, solar energy, and wind energy. Less than one qua...Renewable energy includes all forms of energy produced from renewable sources in a sustainable manner, including bioenergy, geothermal energy, hydropower, ocean energy, solar energy, and wind energy. Less than one quarter of Africa’s renewable power generation potential is utilized. Africa’s natural endowments are enormous, yet the continent experiences high energy shortage. Amongst the classifications of energy sources, renewable and green energy sources are increasingly gaining popularity due to their sustainable nature and environmental concerns. This paper explores the continent’s natural energy sources and identifies pathways to sustainable development. The paper also narrows the renewable and green energy sources obtainable on the continent and presents their contribution to the development of the continent. The awareness level of Africans towards renewable energy is discussed and the challenges of renewable and green energy sources are highlighted. Finally, the roles to be played by the government and private organizations in the development of renewable and green energy sources in Africa are discussed.展开更多
文摘Since the discovery of oceanic manganese nodules during the expedition of the British ocean-going ship Challenger from 1872 to 1876, research and development for seabed manganese nodules have never ceased owing to the huge economic inducements. Manganese nodules are the black or dark brown, spherical or massive Mn-bearing ores, deposits of which are found on the sea bottom. The nodules are a mixture of silicate and insoluble potassium permanganates （also with sub-Ti, Fe and Na permanganates） that contain more than 30 kinds of metallic elements, among which those of greatest economic interest are Mn （27-30%）, Ni（1.25-1.5%）, Cu（1- 1.4%）, Co（0.2- 0.25 %）, Fe, Si, and AI, with minor amounts of Ca, Na, K, Ti, B, H and O.
基金supported by the Guangxi University of Science and Technology,Liuzhou,China,sponsored by the Researchers Supporting Project(No.XiaoKeBo21Z27,The Construction of Electronic Information Team Supported by Artificial Intelligence Theory and ThreeDimensional Visual Technology,Yuesheng Zhao)supported by the Key Laboratory for Space-based Integrated Information Systems 2022 Laboratory Funding Program(No.SpaceInfoNet20221120,Research on the Key Technologies of Intelligent Spatio-Temporal Data Engine Based on Space-Based Information Network,Yuesheng Zhao)supported by the 2023 Guangxi University Young and Middle-Aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0352,Research on the Recognition of Psychological Abnormalities in College Students Based on the Fusion of Pulse and EEG Techniques,Yutong Lu).
文摘The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.
文摘Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.
文摘Renewable energy includes all forms of energy produced from renewable sources in a sustainable manner, including bioenergy, geothermal energy, hydropower, ocean energy, solar energy, and wind energy. Less than one quarter of Africa’s renewable power generation potential is utilized. Africa’s natural endowments are enormous, yet the continent experiences high energy shortage. Amongst the classifications of energy sources, renewable and green energy sources are increasingly gaining popularity due to their sustainable nature and environmental concerns. This paper explores the continent’s natural energy sources and identifies pathways to sustainable development. The paper also narrows the renewable and green energy sources obtainable on the continent and presents their contribution to the development of the continent. The awareness level of Africans towards renewable energy is discussed and the challenges of renewable and green energy sources are highlighted. Finally, the roles to be played by the government and private organizations in the development of renewable and green energy sources in Africa are discussed.
